Data in the Headlights: April 2018

Data In The Headlights

Welcome to another issue of Data in the Headlights. In this issue, the exclusive dataset for you is an influencer analysis of the recent RSA Conference. This security conference is one of the largest in the world, and is a who’s who of people in the cybersecurity and technology world. Conferences are one of our favorite sources for identifying influencers. Why? We know everyone at that event, during that period of time, is relevant to a specific niche or vertical. Conference-based influencer lists also help surface rising stars that might otherwise be drowned out in regular searching by the noisiest (but not as influential) voices.

Using machine learning, we explored more than 70,000 tweets using the conference hashtag and identified 883 entities (out of tens of thousands) who were the most talked about. The graphic shows how different handles interact and cluster together:

PDF map of RSAC influencers, 191 KB >>

The spreadsheet below is what you’d hand to your sales, marketing, social media, or customer experience teams/agencies, depending on how you use influencers.

Microsoft Excel file of RSAC influencers, 75 KB >>

Enjoy this dataset. If you have questions about it, or would like us to develop one for your company based on a recent or upcoming industry event, please let us know by hitting reply now.

The Bright Idea

LinkedIn Shares Go Missing - Fixing Them With Machine Learning

Missing data is the bane of every industry and profession that aspires to be data-driven. Few things are worse, however, than having data and then having access to the data revoked by a third party. This is the situation that content marketers find themselves in today as social networks restrict more and more access to data about how content is shared. On February 7, 2018, LinkedIn removed its sharing counts, blinding many to the performance of their content on the network.

Trust Insights set out to find alternatives for the missing data. With none commercially available, we instead built a methodology for inferring, or imputing, the missing data using machine learning technology with 98.2% accuracy. After extensive testing and validation with a quarter-million-row dataset, we deployed it to help marketers once again understand how their content performed on LinkedIn.

Click here to watch this short video and grab your copy of the white paper >>

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